In a large-scale system such as a cloud system, there are a lot of cases where registration and/or change of the setting information such as a host name and gateway are performed. Therefore, there is a possibility that a lot of troubles such as system failures, which are caused by setting mistakes, occur, however, the reduction of the setting mistakes by human check is limited.
The cloud system includes servers, which are grouped by various kinds of units such as server units, rack units, which respectively include one or plural servers, island units that respectively include one or plural racks, and region units. Typically, for each unit, its setting is almost homogenized.
In order to decrease the setting mistakes, there is a method for extracting a rule that holds true for most of setting parameters, and further routinely extracting setting parameters that do not conform with the extracted rule as an error candidate. For example, data as illustrated in FIG. 1A is assumed. In other words, setting values for parameters 1 and 2 are set for each of setting targets 1 to 7. In such a case, as depicted in a portion surrounded by a dotted line in FIG. 1A, a first rule is extracted that if parameter 1 is “A”, parameter 2 is “B”, and as depicted in a portion surrounded by a dash dotted line in FIG. 1A, a second rule is extracted that if the parameter 1 is “C”, the parameter 2 is “D”, as depicted in FIG. 1B. When the first and second rules are applied to data illustrated in FIG. 1A, it can be understood that the setting target “5” is contrary to the first rule, because the parameter 1 is “A”, however, the parameter 2 is “E”, as illustrated in FIG. 1C. Therefore, because the setting value of the parameter 2 for the setting target “5” has a possibility of the error, it is possible to display, as an error candidate, the setting value of the parameter 2 for the user.
In such a conventional technique, because a rule is generated based on data after setting changes, a rule to appropriately determine the setting error cannot be extracted in a situation that a parameter value for a certain parameter is erroneously set for most of servers. In an example of FIG. 1A, when “A” for the parameter 1 is set for all setting targets, a rule “if parameter 1 is C, parameter 2 is D” is not generated, and the setting targets “2”, “4” and “5” are identified as the setting error candidates.
Currently, in order to reduce the cost and improve the reliability, the automation of the system design and setting change is advanced, and the number of cases increases that a parameter value for a specific setting target is copied and pasted to other setting targets. In such a case, when the parameter value for the specific setting target is wrong, the wrong value is spread to the entire system. However, according to the aforementioned conventional technique, any rule is not appropriately generated, therefore, such a case cannot be handled.
Patent Document 1: Japanese examined Patent application Publication No. 06-1423
Patent Document 2: Japanese Laid-open Patent Publication No. 2004-118371
Patent Document 3: Japanese Laid-open Patent Publication No. 09-289508
Patent Document 4: Japanese Laid-open Patent Publication No. 2007-324941
In other words, there is no technique for appropriately detecting mistakes in the setting change, which occur for most of setting targets.